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2024· paratext· en· W4401107734 on OpenAlexafffund
Stephen Laurence, Eric Margolis

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsComputer science

Abstract

fetched live from OpenAlex

Professor of Philosophy and Director of the Hang Seng Centre for Cognitive Studies Professor of Philosophy The basic reorientation experiment Reorientation experiment variations Variation in the extent of alignment with a target domain Alignment to an extent vs. perfect alignment Parsimony in cognitive development Checkerboard image illustrating colour constancy A problem for the closed process invariance account of innateness The Thatcher illusion Sample stimuli from Xu and Spelke (2000) Neurological development in mutant mice that were genetically engineered to eliminate synaptic transmission (null) and in normal mice (control) Spontaneous pride display following athletic success in sighted (left) and congenitally blind (right) athletes The environmental vertical illusion Hespos and Baillargeon’s (2001b) study Baillargeon’s (1987) drawbridge study Stimuli from experiment 1 in Kellman and Spelke (1983) Habituation stimuli from additional experimental conditions in Kellman and Spelke (1983) that argue against Prinz’s alternative hypothesis Rogers and McClelland’s model of semantic memory Rogers and McClelland’s simulation of the pattern of conceptual differentiation in childhood in which broad categories appear before narrower categories Deep learning image categorization errors The typical structure of the concept learning process for learning a complex concept via hypothesis testing

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.067
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.012
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.9330.887

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.343
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes2
Has abstractno

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Same topicLanguage and cultural evolutionFrench-language works237,207